Introduction to Computational Methods for the Brain Sciences

Department of Brain Sciences
Imperial College London

Contributors:

Dragos Gruia	Valentina Giunchiglia

Module director contact

Adam Hampshire: a.hampshire@imperial.ac.uk

Lead TAs contacts:

Dragos Gruia: dragos-cristian.gruia19@imperial.ac.uk
Valentina Giunchiglia: v.giunchiglia20@imperial.ac.uk

Introduction

Module 3 from the MSc in Translational Neuroscience will introduce you to the most relevant computational methods for the Brain Sciences. It will be divided into 8 days, where each day handles a different thematic and methodology. Each day will consist of workshops and lectures, that start in the morning and end at 5pm.

  Apart from day 1, all the other days will be characterised by a final CHALLENGE. 
  At the end of the first week, you will be randomly assigned one of these challenges, 
  which you have to complete and present in a report, that will be assessed. 
  In the afternoon of each day, you will have the opportunity to start working on 
  the challenge of the day, and ask questions to the TAs in case you have any doubts or problems. 

Lab experiments

To run the lab experiments, you can use Anaconda, Visual Studio Code, or Google CoLab. For more information on how to use Google Colab with GitHub, see this link.

We suggest you to use Jupyter Notebook, and move to Google Colab only if there are significant issues (that cannot be addressed easily) while using Jupyter.

Data

The data for all workshops can be downloaded here.

Schedule

The schedule of the Module is presented in the following table.

Date	Topic	Morning Schedule	Afternoon Schedule	Link to Lecture
9th November	Introduction to Programming	OPTION 1: Review of primer exercises + Q&A on lectures and exercises OPTION 2: Advanced visualization using Seaborn or cognitive data anlysis exercise	Coding Exercises	Introduction to Python    Seaborn:   Coding Exercises:
10th November	Big data analysis: COVID and Cognition	LECTURE: Data cleaning and processing GUIDED WORKSHOP: big data analysis of COVID and Cognition	CHALLENGE: big data analysis of Dementia and Cognition LECTURE: How to write a report?	Covid and Cognition
13th November	Cognition and self harm	LECTURE: Cognitive differences in self harm Lived experience interview GUIDED WORKSHOP: cognition and self harm	CHALLENGE: Cognition and eating disorders	Cognition and self-harm
14th November	Introduction to fMRI and sMRI analysis	GUIDED WORKSHOP: analysis and visualisation of sMRI and fMRI data	CHALLENGE: analysis of finger tapping task fMRI data	Introduction to fMRI and sMRI analysis
15th November	fMRI group level analysis	GUIDED WORKSHOP: analysis of Parkinson's fMRI data	CHALLENGE: analysis of OCD fMRI data	fMRI group level analysis
20th November	fMRI Graph theory and connectivity	GUIDED WORKSHOP: introduction to fMRI graph analysis	CHALLENGE: brain connectivity changes in the psychedelic state	Introduction to fMRI and sMRI analysis
21nd November	Introduction to unsupervised Machine Learning	LECTURE: Substance use and substance addiction LECTURE: Machine Learning (unsupervised) GUIDED WORKSHOP: Drug use and mental health	CHALLENGE: Drug use and cognition	Introduction to unsupervised ML
22rd November	Introduction to supervised Machine Learning	LECTURE: Machine Learning (supervised) GUIDED WORKSHOP: Alzheimer's prediction using machine learning	CHALLENGE: prediction of Alzheimer's progression	Introduction to supervised ML